Effect of B-value in revealing postinfarct myocardial microstructural remodeling using MR diffusion tensor imaging

Anesthetic modulation of water diffusion: Insights from a diffusion tensor imaging study

Diffusion tensor imaging (DTI) in animal models are essential for translational neuroscience studies. A critical step in animal studies is the use of anesthetics. Understanding the influence of specific anesthesia regimes on DTI-derived parameters, such as fractional anisotropy (FA) and mean diffusivity (MD), is imperative when comparing results between animal studies using different anesthetics. Here, the quantification of FA and MD under different anesthetic regimes, alpha-chloralose and isoflurane, is discussed. We also used a range of b-values to determine whether the anesthetic effect was b-value dependent. The first group of rats (n = 6) was anesthetized with alpha-chloralose (80 mg/kg), whereas the second group of rats (n = 7) was anesthetized with isoflurane (1.5%). DTI was performed with b-values of 500, 1500, and 1500s/mm2, and the MD and FA were assessed individually. Anesthesia-specific differences in MD were apparent, as manifested by the higher estimated MD under isoflurane anesthesia than that under alpha-chloralose anesthesia (P < 0.001). MD values increased with decreasing b-value in all regions studied, and the degree of increase when rats were anesthetized with isoflurane was more pronounced than that associated with alpha-chloralose (P < 0.05). FA quantitation was also influenced by anesthesia regimens to varying extents, depending on the brain regions and b-values. In conclusion, both scanning parameters and the anesthesia regimens significantly impacted the quantification of DTI indices.

Cardiac q-space trajectory imaging by motion-compensated tensor-valued diffusion encoding in human heart in vivo

PURPOSE

Tensor-valued diffusion encoding can probe more specific features of tissue microstructure than what is available by conventional diffusion weighting. In this work, we investigate the technical feasibility of tensor-valued diffusion encoding at high b-values with q-space trajectory imaging (QTI) analysis, in the human heart in vivo.

METHODS

Ten healthy volunteers were scanned on a 3T scanner. We designed time-optimal gradient waveforms for tensor-valued diffusion encoding (linear and planar) with second-order motion compensation. Data were analyzed with QTI. Normal values and repeatability were investigated for the mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (μFA), isotropic, anisotropic and total mean kurtosis (MKi, MKa, and MKt), and orientation coherence (Cc ). A phantom, consisting of two fiber blocks at adjustable angles, was used to evaluate sensitivity of parameters to orientation dispersion and diffusion time.

RESULTS

QTI data in the left ventricular myocardium were MD = 1.62 ± 0.07 μm2 /ms, FA = 0.31 ± 0.03, μFA = 0.43 ± 0.07, MKa = 0.20 ± 0.07, MKi = 0.13 ± 0.03, MKt = 0.33 ± 0.09, and Cc  = 0.56 ± 0.22 (mean ± SD across subjects). Phantom experiments showed that FA depends on orientation dispersion, whereas μFA was insensitive to this effect.

CONCLUSION

We demonstrated the first tensor-valued diffusion encoding and QTI analysis in the heart in vivo, along with first measurements of myocardial μFA, MKi, MKa, and Cc . The methodology is technically feasible and provides promising novel biomarkers for myocardial tissue characterization.

Both noise-floor and tissue compartment difference in diffusivity contribute to FA dependence on b-value in diffusion MRI

Noninvasive diffusion magnetic resonance imaging (dMRI) has been widely employed in both clinical and research settings to investigate brain tissue microstructure. Despite the evidence that dMRI-derived fractional anisotropy (FA) correlates with white matter properties, the metric is not specific. Recent studies have reported that FA is dependent on the b-value, and its origin has primarily been attributed to either the influence of microstructure or the noise-floor effect. A systematic investigation into the inter-relationship of these two effects is however still lacking. This study aims to quantify contributions of the reported differences in intra- and extra-neurite diffusivity to the observed changes in FA, in addition to the noise in measurements. We used in-vivo and post-mortem human brain imaging, as well as numerical simulations and histological validation, for this purpose. Our investigations reveal that the percentage difference of FA between b-values (pdFA) has significant positive associations with neurite density index (NDI), which is derived from in-vivo neurite orientation dispersion and density imaging (NODDI), or Bielschowsky's silver impregnation (BIEL) staining sections of fixed post-mortem human brain samples. Furthermore, such an association is found to be varied with Signal-to-Noise Ratio (SNR) level, indicating a nonlinear interaction effect between tissue microstructure and noise. Finally, a multicompartment model simulation revealed that these findings can be driven by differing diffusivities of intra- and extra-neurite compartments in tissue, with the noise-floor further amplifying the effect. In conclusion, both the differences in intra- and extra-neurite diffusivity and noise-floor effects significantly contribute to the FA difference associated with the b-value.

Effect of b Value on Imaging Quality for Diffusion Tensor Imaging of the Spinal Cord at Ultrahigh Field Strength

Objective

To explore the optimal b value setting for diffusion tensor imaging of rats' spinal cord at ultrahigh field strength (7 T).

Methods

Spinal cord diffusion tensor imaging data were collected from 14 rats (5 healthy, 9 spinal cord injured) with a series of b values (200, 300, 400, 500, 600, 700, 800, 900, and 1000 s/mm²) under the condition that other scanning parameters were consistent. The image quality (including image signal-to-noise ratio and image distortion degree) and data quality (i.e., the stability and consistency of the DTI-derived parameters, referred to as data stability and data consistency) were quantitatively evaluated. The min-max normalization method was used to process the calculation results of the four indicators. Finally, the image and data quality under each b value were synthesized to determine the optimal b value.

Results

b = 200 s/mm² and b = 900 s/mm² ranked in the top two of the comprehensive evaluation, with the best image quality at b = 200 s/mm² and the best data quality at b = 900 s/mm².

Conclusion

Considering the shortcomings of the ability of low b values to reflect the microstructure, b = 900 s/mm² can be used as the optimal b value for 7 T spinal cord diffusion tensor scanning.

Functional Assessment of Lumbar Nerve Roots Using Coronal-plane Single-shot Turbo Spin-echo Diffusion Tensor Imaging

We investigated the usefulness of diffusion tensor imaging using single-shot turbo spin-echo sequence (TSE–DTI) in detecting the responsible nerve root by multipoint measurements of fractional anisotropy (FA) values. Five patients with bilateral lumbar spinal stenosis showing unilateral neurological symptoms were examined using TSE–DTI. In the spinal canal, FA values in the symptomatic side were lower than those in the asymptomatic side. TSE–DTI using multipoint measurements of FA values can differentiate the responsible lumbar nerve root.

Evaluation of non-Gaussian diffusion in cardiac MRI

PURPOSE

The diffusion tensor model assumes Gaussian diffusion and is widely applied in cardiac diffusion MRI. However, diffusion in biological tissue deviates from a Gaussian profile as a result of hindrance and restriction from cell and tissue microstructure, and may be quantified better by non-Gaussian modeling. The aim of this study was to investigate non-Gaussian diffusion in healthy and hypertrophic hearts.

METHODS

Thirteen rat hearts (five healthy, four sham, four hypertrophic) were imaged ex vivo. Diffusion-weighted images were acquired at b-values up to 10,000 s/mm² . Models of diffusion were fit to the data and ranked based on the Akaike information criterion.

RESULTS

The diffusion tensor was ranked best at b-values up to 2000 s/mm² but reflected the signal poorly in the high b-value regime, in which the best model was a non-Gaussian "beta distribution" model. Although there was considerable overlap in apparent diffusivities between the healthy, sham, and hypertrophic hearts, diffusion kurtosis and skewness in the hypertrophic hearts were more than 20% higher in the sheetlet and sheetlet-normal directions.

CONCLUSION

Non-Gaussian diffusion models have a higher sensitivity for the detection of hypertrophy compared with the Gaussian model. In particular, diffusion kurtosis may serve as a useful biomarker for characterization of disease and remodeling in the heart. Magn Reson Med 78:1174-1186, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Adaptive anisotropic gaussian filtering to reduce acquisition time in cardiac diffusion tensor imaging

Diffusion tensor imaging (DTI) is used to quantify myocardial fiber orientation based on helical angles (HA). Accurate HA measurements require multiple excitations (NEX) and/or several diffusion encoding directions (DED). However, increasing NEX and/or DED increases acquisition time (TA). Therefore, in this study, we propose to reduce TA by implementing a 3D adaptive anisotropic Gaussian filter (AAGF) on the DTI data acquired from ex-vivo healthy and infarcted porcine hearts. DTI was performed on ex-vivo hearts [9-healthy, 3-myocardial infarction (MI)] with several combinations of DED and NEX. AAGF, mean (AVF) and median filters (MF) were applied on the primary eigenvectors of the diffusion tensor prior to HA estimation. The performance of AAGF was compared against AVF and MF. Root mean square error (RMSE), concordance correlation-coefficients and Bland-Altman's technique was used to determine optimal combination of DED and NEX that generated the best HA maps in the least possible TA. Lastly, the effect of implementing AAGF on the infarcted porcine hearts was also investigated. RMSE in HA estimation for AAGF was lower compared to AVF or MF. Post-filtering (AAGF) fewer DED and NEX were required to achieve HA maps with similar integrity as those obtained from higher NEX and/or DED. Pathological alterations caused in HA orientation in the MI model were preserved post-filtering (AAGF). Our results demonstrate that AAGF reduces TA without affecting the integrity of the myocardial microstructure.

Optimal preservation of porcine cardiac tissue prior to diffusion tensor magnetic resonance imaging

The effects of ex vivo preservation techniques on the quality of diffusion tensor magnetic resonance imaging in hearts are poorly understood, and the optimal handling procedure prior to investigation remains to be determined. Therefore, 24 porcine hearts were examined in six groups treated with different preservation techniques, including chemical fixation and freezing. Diffusion properties of each heart were assessed with diffusion tensor imaging in terms of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da) and radial diffusivity (Dr). Tractography was performed to visualize the course of the cardiomyocytes, assuming greater diffusivity in the longitudinal than the transverse axis of individual cardiomyocytes. Significant differences in MD, Da and Dr were found, as well as in FA between groups (P < 0.001). Freezing of specimens resulted in the lowest mean FA of 0.21 (0.06) and highest Dr of 8.92 (1.5) mm2 s(-1) . The highest mean FA was found to be 0.43 (0.11) in hearts perfusion-fixed with formalin. Calculated tractographies were indistinguishable among groups except in frozen specimens, where no fibres could be tracked. Perfusion fixation with formalin provided the best tractography, but immersion fixation yielded diffusion data most similar to fresh hearts. These findings suggest that parameters derived from diffusion tensor imaging in ex vivo hearts are sensitive to fixation and storage methods. In particular, freezing of specimens should be avoided prior to diffusion tensor imaging investigation due to significant changes in diffusion parameters and subsequent image deteriorations.

Optimal diffusion weighting for in vivo cardiac diffusion tensor imaging

PURPOSE

To investigate the influence of the diffusion weighting on in vivo cardiac diffusion tensor imaging (cDTI) and obtain optimal parameters.

METHODS

Ten subjects were scanned using stimulated echo acquisition mode echo planar imaging with six b-values, from 50 to 950 s·mm(-2) , plus b = 15 s·mm(-2) reference. The relationship between b-value and both signal loss and signal-to-noise ratio measures was investigated. Mean diffusivity, fractional anisotropy, and helical angle maps were calculated using all possible b-value pairs to investigate the effects of diffusion weighting on the main and reference data.

RESULTS

Signal decay at low b-values was dominated by processes with high apparent diffusion coefficients, most likely microvascular perfusion. This effect could be avoided by diffusion weighting of the reference images. Parameter maps were improved with increased b-value until the diffusion-weighted signal approached the noise floor. For the protocol used in this study, b = 750 s·mm(-2) combined with 150 s·mm(-2) diffusion weighting of the reference images proved optimal.

CONCLUSION

Mean diffusivity, fractional anisotropy, and helical angle from cDTI are influenced by the b-value of the main and reference data. Using optimal values improves parameter maps and avoids microvascular perfusion effects. This optimized protocol should provide greater sensitivity to pathological changes in parameter maps.

Enhancement of the low resolution image quality using randomly sampled data for multi-slice MR imaging

Low resolution images are often acquired in in vivo MR applications involving in large field-of-view (FOV) and high speed imaging, such as, whole-body MRI screening and functional MRI applications. In this work, we investigate a multi-slice imaging strategy for acquiring low resolution images by using compressed sensing (CS) MRI to enhance the image quality without increasing the acquisition time. In this strategy, low resolution images of all the slices are acquired using multiple-slice imaging sequence. In addition, extra randomly sampled data in one center slice are acquired by using the CS strategy. These additional randomly sampled data are multiplied by the weighting functions generated from low resolution full k-space images of the two slices, and then interpolated into the k-space of other slices. In vivo MR images of human brain were employed to investigate the feasibility and the performance of the proposed method. Quantitative comparison between the conventional low resolution images and those from the proposed method was also performed to demonstrate the advantage of the method.